[P] asmsense: 228k stripped x86-64 functions with source-aware labels, and a map of the data we still lack. [P]
asmsense is a dataset and a small model (Qwen3-8B + LoRA) for naming and describing functions in stripped binaries.
Proof1 community thread
Key points
- I compiled permissively licensed C projects myself, so every function has its real name and source.
- In short: other architectures (ARM64, RISC-V, MIPS), GCC/ELF builds (so far everything is clang/Mach-O on macOS), Chinese descriptions, human-checked labels, agent trajectories, and runs on public benchmarks like BinMetric, REBench, BinSum and DeBinVul.
- I’m thinking about turning the pipeline into an RL environment: compile, strip, let the model name the function, and score it against the real name with token-level F1.
- Which of the gaps above matters most for a model that has to run offline?
Sources (1)
- [1][P] asmsense: 228k stripped x86-64 functions with source-aware labels, and a map of the data we still lack. [P]r/MachineLearning (top, daily) · Oct 18, 11:49 AM
asmsense is a dataset and a small model (Qwen3-8B + LoRA) for naming and describing functions in stripped binaries.
I compiled permissively licensed C projects myself, so every function has its real name and source.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 17, 2026Blown away by Minimax H3 sometimes. It nailed this one!!!!
- Oct 17, 2026EpochGo: Local-first multi-agent AutoML engine using offline Ollama LLMs and deterministic Scikit-Learn pipelines [P]
- Oct 17, 2026Otaku — an LLM frontend for creative writing
- Oct 16, 2026Cross-Centaur: Modeling Individual Decision-Making Across Psychological Tasks with Language Models
- Oct 15, 2026Granite Switch 4.1: Chat, Decisions, and Speech from One Endpoint
- Oct 15, 2026NVIDIA Nemotron 3.5 Super VL Powers Specialized Agents with Multimodal Reasoning